The main objective of the course is to provide the fundamental tools for the application of statistical methods to the analysis of economic data. The theoretical part will be supported by an applied part devoted to the analysis of real data sets by means of the software R. A student that has completed the course should be practiced in the application of advanced statistical methods, should be able to interpret the results of a statistical analysis, and should be aware of limitations and possible sources of errors in the analysis.
Curriculum
teacher profile teaching materials
- Cluster analysis
- Principal component analysis
Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic regression
- Beta regression
- Poisson and negative binomial regression for count data
- Spatial regression models
Part 3: Panel data analysis
- Fixed effects and random effects models for dicotomous, categorical and continuous variables
- Spatial regression models for panel data.
Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications Inc.
Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4
Programme
Part I: Introduction to data analysis and exploratory tecniques- Cluster analysis
- Principal component analysis
Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic regression
- Beta regression
- Poisson and negative binomial regression for count data
- Spatial regression models
Part 3: Panel data analysis
- Fixed effects and random effects models for dicotomous, categorical and continuous variables
- Spatial regression models for panel data.
Core Documentation
Chatterjee,S. and Hadi, A.S. (2012), Regression Analysis by Example, 5th Edition, Wiley. Chapters: 1, 2, 3 (excluding 3.9), 4 (excluding 4.3, 4.9.2, 4.9.3, 4.10, 4.12, 4.13, 4.14), 5 (excluding 5.6 and 5.7), 6 (excluding 6.6 and 6.7), 9, 11, 12 (excluding 12.8.3 and 12.8.4), 13 (excluding 13.5, 13.6, 13.7).Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications Inc.
Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4
Reference Bibliography
J. Paul Elhorst (2010). Applied Spatial Econometrics: Raising the Bar, Spatial Economic Analysis, 5:1, 9-28. Cribari-Neto F, Zeileis A (2010). “Beta Regression in R.” Journal of Statistical Software, 34(2), 1–24. Adelchi Azzalini, Bruno Scarpa (2012). Data analysis and data mining: an Introduction. Oxford University Press.Type of delivery of the course
6 lecture hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam in the Computer lab. Only in January and February 2024 attending students can develop and discuss a short dissertation in place of the written exam in the Computer lab. teacher profile teaching materials
- Cluster analysis
- Principal component analysis
Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic regression
- Beta regression
- Poisson and negative binomial regression for count data
- Spatial regression models
Part 3: Panel data analysis
- Fixed effects and random effects models for dicotomous, categorical and continuous variables
- Spatial regression models for panel data.
Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications Inc.
Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4
Mutuazione: 21210065 Statistical methods in economics in Economia dell'ambiente, lavoro e sviluppo sostenibile LM-56 CONIGLIANI CATERINA
Programme
Part I: Introduction to data analysis and exploratory tecniques- Cluster analysis
- Principal component analysis
Part 2: Normal linear regression and its generalizations
- Polynomial regression
- Multiple regression
- Logistic regression
- Beta regression
- Poisson and negative binomial regression for count data
- Spatial regression models
Part 3: Panel data analysis
- Fixed effects and random effects models for dicotomous, categorical and continuous variables
- Spatial regression models for panel data.
Core Documentation
Chatterjee,S. and Hadi, A.S. (2012), Regression Analysis by Example, 5th Edition, Wiley. Chapters: 1, 2, 3 (excluding 3.9), 4 (excluding 4.3, 4.9.2, 4.9.3, 4.10, 4.12, 4.13, 4.14), 5 (excluding 5.6 and 5.7), 6 (excluding 6.6 and 6.7), 9, 11, 12 (excluding 12.8.3 and 12.8.4), 13 (excluding 13.5, 13.6, 13.7).Fox,J. and Weisberg, S. (2010), An R companion to applied regression, 2nd Edition, SAGE publications Inc.
Andreb, H-J, Golsch, K., Schmidt, A.W. (2013), Applied panel data analysis for economic and social
surveys, Springer. Chapters: 1, 2, 3, 4
Reference Bibliography
J. Paul Elhorst (2010). Applied Spatial Econometrics: Raising the Bar, Spatial Economic Analysis, 5:1, 9-28. Cribari-Neto F, Zeileis A (2010). “Beta Regression in R.” Journal of Statistical Software, 34(2), 1–24. Adelchi Azzalini, Bruno Scarpa (2012). Data analysis and data mining: an Introduction. Oxford University Press.Type of delivery of the course
6 lecture hours per week.Attendance
Strongly recommended.Type of evaluation
Written exam in the Computer lab. Only in January and February 2024 attending students can develop and discuss a short dissertation in place of the written exam in the Computer lab.